A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China) |
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| Authors: | " target="_blank">Haoyuan Hong Junzhi Liu A-Xing Zhu Himan Shahabi Binh Thai Pham Wei Chen Biswajeet Pradhan Dieu Tien Bui |
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| Institution: | 1.Key Laboratory of Virtual Geographic Environment (Nanjing Normal University),Ministry of Education,Nanjing,China;2.State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province),Nanjing,China;3.Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application,Nanjing,China;4.Department of Geomorphology, Faculty of Natural Resources,University of Kurdistan,Sanandaj,Iran;5.Department of Geotechnical Engineering,University of Transport Technology,Hanoi,Vietnam;6.College of Geology and Environment,Xi’an University of Science and Technology,Xi’an,China;7.Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering,University Putra Malaysia,Serdang,Malaysia;8.School of Systems, Management and Leadership, Faculty of Engineering and Information Technology,University of Technology Sydney,Ultimo,Australia;9.Geographic Information System group, Department of Business and IT,University College of Southeast Norway,B?i Telemark,Norway |
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| Abstract: | This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area. |
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